Reconfiguration and Capacitor Placement for Loss Reduction of Distribution Systems by Ant Colony Search Algorithm

This paper aims to study distribution system operations by the ant colony search algorithm (ACSA). The objective of this study is to present new algorithms for solving the optimal feeder reconfiguration problem, the optimal capacitor placement problem, and the problem of a combination of the two. The ACSA is a relatively new and powerful swarm intelligence method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as "greedy" search. The ACSA was inspired from the natural behavior of ants in locating food sources and bring them back to their colony by the formation of unique trails. Therefore, through a collection of cooperative agents called "ants," the near-optimal solution to the feeder reconfiguration and capacitor placement problems can be effectively achieved. In addition, the ACSA applies the state transition, local pheromone-updating, and global pheromone-updating rules to facilitate the computation. Through simultaneous operation of population agents, process stagnation can be effectively prevented. Optimization capability can be significantly enhanced. The proposed approach is demonstrated using two example systems from the literature. Computational results show that simultaneously, taking into account both feeder reconfiguration and capacitor placement is more effective than considering them separately.